MultiAuth: Enable Multi-User Authentication with Single Commodity WiFi Device
Published in ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc 2021), 2021
Recommended citation: Hao Kong, Li Lu, Jiadi Yu, Yingying Chen, Xiangyu Xu, Feilong Tang, Yi-Chao Chen. "MultiAuth: Enable Multi-User Authentication with Single Commodity WiFi Device." Proceedings of ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing (ACM MobiHoc). Shanghai, China. pp. 31-40. 2021. doi: 10.1145/3466772.3467032.
ACM International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing is the premier international conference in mobile networking and computing. ACM MobiHoc is also a CCF-B conference.
Abstract: With the increasing integration of humans and the cyber world, user authentication becomes critical to support various emerging application scenarios requiring security guarantees. Existing works utilize Channel State Information (CSI) of WiFi signals to capture single human activities for non-intrusive and device-free user authentication, but multi-user authentication remains a challenging task. In this paper, we present a multi-user authentication system, MultiAuth, which can authenticate multiple users with a single commodity WiFi device. The key idea is to profile multipath components of WiFi signals induced by multiple users, and construct individual CSI from the multipath components to solely characterize each user for user authentication. Specifically, we propose a MUltipath Time-of-Arrival measurement algorithm (MUTA) to profile multipath components of WiFi signals in high resolution. Then, after aggregating and separating the multipath components related to users, MultiAuth constructs individual CSI based on the multipath components to solely characterize each user. To identify users, MultiAuth further extracts user behavior profiles based on the individual CSI of each user through time-frequency analysis, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that MultiAuth is accurate and reliable for multi-user authentication with 87.6% average accuracy and 8.8% average false accept rate.